okane111189/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-foraging_lumbering_chameleon
TEXT GENERATIONPricing:Input $0.04 / Cached $0.008 / Output $0.08Concurrent Unit Cost:1Model Size:0.5BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Apr 7, 2025Architecture:Transformer Featherless Exclusive Warm
The okane111189/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-foraging_lumbering_chameleon model is a 0.5 billion parameter instruction-tuned causal language model, fine-tuned from Gensyn/Qwen2.5-0.5B-Instruct. It was trained using the TRL library and the GRPO method, which is designed to enhance mathematical reasoning. This model is optimized for tasks requiring improved mathematical reasoning capabilities, building upon the Qwen2.5 architecture.
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Overview
This model, okane111189/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-foraging_lumbering_chameleon, is a 0.5 billion parameter instruction-tuned language model. It is a fine-tuned version of the Gensyn/Qwen2.5-0.5B-Instruct base model, developed by okane111189.
Key Training Details
- Base Model: Gensyn/Qwen2.5-0.5B-Instruct
- Training Framework: Utilizes the TRL library for fine-tuning.
- Training Method: Employs GRPO (Gradient-based Reward Policy Optimization), a method introduced in the paper "DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models". This suggests a focus on enhancing mathematical reasoning abilities.
Potential Use Cases
- Mathematical Reasoning: Due to its training with the GRPO method, this model is likely to perform well in tasks that require mathematical problem-solving and reasoning.
- Instruction Following: As an instruction-tuned model, it is designed to follow user prompts and generate relevant responses.
- Small-Scale Applications: With 0.5 billion parameters, it is suitable for applications where computational resources are limited, offering a balance between performance and efficiency.